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Hydrometeorological Analysis and Forecasting of a 3-Day Flash-Flood- Triggering Desert Rainstorm

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Hydrometeorological Analysis and Forecasting of a 3-Day Flash-Flood- Triggering Desert Rainstorm https://doi.org/10.5194/nhess-2020-189 Preprint. Discussion started: 2 July 2020 c Author(s) 2020. CC BY 4.0 License. Hydrometeorological analysis and forecasting of a 3-day flash-flood- triggering desert rainstorm 5 Yair Rinat1,*, Francesco Marra1,2, Moshe Armon1, Asher Metzger1, Yoav Levi3, Pavel Khain3, Elyakom Vadislavsky3, Marcelo Rosensaft4, Efrat Morin1 (1) Institute of Earth Sciences, The Hebrew University of Jerusalem, Jerusalem, Israel (2) National Research Council of Italy, Institute of Atmospheric Sciences and Climate, CNR-ISAC, Bologna, Italy 10 (3) Israel Meteorological Service, Beit Dagan, Israel (4) Geological Survey of Israel, Jerusalem, Israel Correspondence to: Yair Rinat ([email protected]) Abstract: Flash floods are among the most devastating and lethal natural hazards. In 2018, three flash-flood episodes 15 resulted in 46 casualties in the deserts of Israel and Jordan alone. This paper presents the hydrometeorological analysis and forecasting of a substantial storm (25–27 Apr 2018) that hit an arid desert basin (Zin, ~1400 km2, southern Israel), claiming 12 human lives. Our aim was to: (a) spatially assess the severity of the storm, (b) quantify the time scale of the hydrological response, and (c) evaluate the available operational precipitation forecasting. Return periods of the storm's maximal rain intensities were derived locally, at 1-km2 resolution, using weather radar data and a novel statistical methodology. A high- 20 resolution grid-based hydrological model was used to study the intra-basin flash-flood magnitudes, which were consistent with direct information from witnesses. The model was further used to examine the hydrological response to different forecast scenarios. A small portion of the basin (1–20%) experienced extreme precipitation intensities (75- to 100-year return period), resulting in a local hydrological response of a high magnitude (10- to 50-year return period). Hillslope runoff, initiated minutes after the intense rainfall occurred, reached the streams and resulted in peak discharge within tens of 25 minutes. Available deterministic operational precipitation forecasts poorly predicted the hydrological response in the studied basins (tens to hundreds of km2) mostly due to location inaccuracy. There was no gain from assimilating radar estimates in the numerical weather-prediction model. Therefore, we suggest using deterministic forecasts with caution as it might lead to fatal decision making. To cope with such errors a novel cost-effective methodology is applied by spatially shifting the forecasted precipitation fields. In this way, flash-flood occurrences were captured in most of the sub-basins, resulting in few 30 false alarms. 1 https://doi.org/10.5194/nhess-2020-189 Preprint. Discussion started: 2 July 2020 c Author(s) 2020. CC BY 4.0 License. 1. Introduction Flash floods are rapidly evolving events characterized by a sudden rise in stream-water level and discharge (Sene, 2013). These events often result in casualties and damage (Barredo, 2007; Borga et al., 2019; Doocy et al., 2013; Grodek et al., 35 2012; Petrucci et al., 2019; Vinet et al., 2019), and are ranked among the most devastating natural hazards worldwide (Barredo, 2007; Borga et al., 2014; Doocy et al., 2013; Gaume et al., 2009; Gruntfest and Handmer, 2001; Marchi et al., 2010; Sene, 2013). Flash-flood conditions are frequently met in arid and semiarid regions (Nicholson, 2011; Pilgrim et al., 1988; Schick, 1988; Simmers, 2003; Tooth, 2000), as these areas are generally characterized by localized high rainfall intensities (Sharon, 1972), 40 low precipitation interception and low infiltration rates due to sparse vegetation coverage (Danin, 1983), and exposed bedrock surfaces that are partially covered by shallow, clay-rich, undeveloped soils (Singer, 2007). Although arid and semiarid regions cover more than a third of the world’s land area, knowledge of flash-flood-generating rainfall properties, hydrological response, and flood-forecasting skills in these areas is limited due to poor measurements, sparse documentation, and a relatively small number of studies (Armon et al., 2018; Nicholson, 2011; Simmers, 2003; Yang et al., 2017; Zoccatelli 45 et al., 2019). Rainstorm patterns in arid regions are characterized by localized structures of high rainfall intensities, termed convective rain cells (e.g., Karklinsky and Morin, 2006; Morin and Yakir, 2014; Nicholson, 2011; Sharon, 1972). During rainstorms, one or more convective rain cells can deliver relatively large rainfall amounts over small areas in a short time, directly contributing to runoff initiation and flash-flood occurrence (Archer et al., 2007; Borga et al., 2007; Chappell, 1986; Delrieu et al., 2005; 50 Doswell et al., 1996; Gaume et al., 2016; Marchi et al., 2010; Yakir and Morin, 2011). Yakir and Morin (2011) found that flash floods in arid regions can occur as a result of a single rain cell, and that the flood’s magnitude is sensitive to its starting location, direction, and velocity. Belachsen et al. (2017) found that, in arid regions, storms that generate flash floods are characterized by rain cells with larger area, lower advection velocity, and longer lifetime than storms that do not produce flash floods. Furthermore, rain-gauge networks are unable to adequately sample the spotty precipitation patterns (Faurès et 55 al., 1995; Kampf et al., 2018; Michaud and Sorooshian, 1994; Wheater et al., 2007), limiting our knowledge of rainfall climatology and frequency in arid regions (Marra et al., 2019b; Marra and Morin, 2015). As a result of the complex rainfall patterns, runoff in arid and semiarid regions is often unexpected, localized, and characterized by high temporal variability (Morin et al., 2009b; Nicholson, 2011). Hillslope runoff does not always reach the stream network (Shmilovitz et al., 2020; Yair et al., 1980; Yair and Kossovsky, 2002; Yair and Raz-Yassif, 2004) and 60 transmission losses can further enhance the intra-basin complexities and runoff localization (Greenbaum et al., 2002; Morin et al., 2009a; Walters, 1990). Even in the most extreme events, only part of the basin contributes runoff (Nicholson, 2011; Pilgrim et al., 1988; Yang et al., 2017). 2 https://doi.org/10.5194/nhess-2020-189 Preprint. Discussion started: 2 July 2020 c Author(s) 2020. CC BY 4.0 License. The devastating effect of flash floods is attributed not only to the magnitude of the event, but also to their fast development and unexpected occurrence. When individuals or communities are not aware of the approaching danger, they are unable to 65 escape or protect themselves (e.g., Borga et al., 2019; Creutin et al., 2013). Thus, effective early warning of flash floods greatly depends on the time between the center of mass of the excess rainfall and the peak discharge (Dingman, 2015; USGS, 2012), termed lag time, and the time available between the issuing of the forecast and the peak discharge (Sene, 2013), termed lead time. The former is dictated by nature, while the latter also depends on the accuracy and effectiveness of the forecasting chain. Creutin et al. (2013) and Marchi et al. (2010) calculated lag times for Europe under various climate 70 regimes, and found that it increases with basin area and follows a general power-law behavior, where basins in areas smaller than 100 km2 often had lag times of less than 1 h. Zoccatelli et al. (2019) found that the mean lag time for 14 arid basins (202–1232 km2) in Israel is on the order of tens of minutes to several hours. To increase flash-flood predictability and extend the lead time, accurate rainfall forecasting is required (Alfieri et al., 2012; Sene, 2013). Commonly used methods include weather-prediction models and nowcasting techniques. Global weather- 75 prediction models are routinely used by meteorological agencies worldwide, but their spatiotemporal scales are too coarse for flash-flood applications (Sene, 2013). In recent years, convection-permitting models with spatial resolution of ≤3 km have enabled explicit representation of the convective process, providing better representation of rainfall and better forecast skills on the flash-flood scale (Armon et al., 2020; Clark et al., 2016; Khain et al., 2019; Prein et al., 2015). However, the finer scale increases the sensitivity of these models to initial conditions, leading to spatial uncertainties in their output 80 (Bartsotas et al., 2016; Ben Bouallègue and Theis, 2014; Collier, 2007; Sivakumar, 2017). To cope with these limitations, radar rainfall estimates are routinely assimilated into the models (Clark et al., 2016; Stephan et al., 2008). Nevertheless, spatial and temporal uncertainties in individual forecasts are still observed, and multiple model runs should therefore be considered in a probabilistic ensemble framework (Ben Bouallègue and Theis, 2014; Dey et al., 2016). In general, flash floods remain a poorly understood and documented process (Borga et al., 2019; Foody et al., 2004; Gaume 85 et al., 2009; Nicholson, 2011; Wheater et al., 2007) despite their devastation potential (Borga et al., 2019; Gaume et al., 2009; Inbar, 2019; Tarolli et al., 2012; Zekai, 2008) and increasing impact (Doocy et al., 2013; Wittenberg et al., 2007), especially in arid areas (Zoccatelli et al., 2019, 2020). The present work aims to increase our understanding and knowledge of desert flash floods and to test practical forecasting abilities by presenting a comprehensive study of the rainstorm of 25–27 Apr 2018 that hit the arid Zin basin in southern Israel (1400
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